All the analysis and numerical results are obtained using evolutionary game theory (EGT) methods for finite populations [1]}, [2]}. In such a setting, agents' payoff represents their fitness or social success, and evolutionary dynamics is shaped by social learning [3]}, [4]}, whereby the most successful agents will tend to be imitated more often by the other agents. In the current work, social learning is modeled using the so-called pairwise comparison rule [5]}, a standard approach in EGT, assuming that an agent \(A\)  with fitness \(f_A\)  adopts the strategy of another agent \(B\)  with fitness \(f_B\)  with probability \(p\)  given by the Fermi function,
\(p_{A, B}=\left(1 + e^{-\beta (f_B-f_A)}\right)^{-1}.\) 
The parameter \(\beta \)  represents the `imitation strength' or `intensity of selection', i.e., how strongly the agents base their decision to imitate on fitness difference between themselves and the opponents. For \(\beta =0\) , we obtain the limit of neutral drift – the imitation decision is random. For large \(\beta \) , imitation becomes increasingly deterministic.
